Training course · Perplexity · Finance & Treasury

Perplexity for Finance Teams

AI-powered research with Perplexity: cited answers, source control, Spaces, Deep Research and systematic fact-checking.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Perplexity with the everyday processes of Finance & Treasury. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Perplexity combines language models with live web search and delivers answers backed by numbered citations. That makes it particularly suited to work where currency and traceability matter: market and competitor monitoring, research into regulation and standards, supplier and partner due diligence, or preparing decision papers. In this practical day you learn to phrase research questions precisely, steer the choice of sources deliberately and use follow-up questions to turn an initial answer into a robust research result.

You work with Spaces, where you group research on a topic, add your own files and set instructions for the team. With Deep Research you produce extensive reports that evaluate many sources and bring them together in a structured way. Quality is a key focus: you learn to assess sources, cross-check claims and recognise typical failure patterns such as outdated or misattributed information. We also address data protection and licensing questions for business use. You leave with a reusable research process tailored to your own function.

Finance and treasury teams handle large volumes of data, tight closing deadlines and strict demands for traceability. Generative AI is reshaping this work: AI assistants summarise bank statements, loan agreements and covenant clauses, draft commentary on cash flow variances and support scenario analysis on interest and exchange rates. AI agents and automation take over recurring steps such as matching payment advices, preparing the liquidity forecast or assembling management reports. What matters is that finance professionals review the output, connect data sources properly and define clear approval workflows. That frees finance teams to focus on analysis, steering and advising management instead of copying figures between spreadsheets, the ERP system and banking portals.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • CFOs and heads of finance
  • Treasury and cash managers
  • Financial analysts and FP&A teams
  • Corporate finance and M&A specialists
  • Payments and banking operations staff

Typical challenges we address

  • Consolidating the liquidity forecast from many sources such as ERP, bank accounts and business units
  • Explaining and commenting on budget-to-actual variances quickly
  • Reviewing loan agreements, covenants and bank correspondence under time pressure
  • Producing reports for executives, the supervisory board and lenders at short notice

What you will be able to do

  • You phrase precise research questions and steer the choice of sources deliberately.
  • You assess sources by origin, currency and reliability and trace claims back to the original.
  • You organise research in Spaces and combine web sources with your own documents.
  • You use Deep Research to produce structured reports and decision support.
  • Use AI assistants effectively for liquidity planning, variance analysis and financing documents.
  • Automate recurring treasury tasks with clear review and approval steps.
  • Critically assess AI output and keep financial data confidential and audit-proof.

Course outline

Module 1 Foundations of AI-powered research
  • How it works: language model, live web search and citations
  • How it differs from a classic search engine and a general chat assistant
  • Framing research questions precisely: time frame, region, source type
  • Follow-ups and drilling down instead of one-off queries
Module 2 Steering and assessing sources
  • Narrowing sources: web, academic literature, your own files
  • Targeting primary sources, official websites and specialist publications
  • Assessing source quality: origin, currency, vested interests
  • Tracing quotes and figures back to the original source
Module 3 Spaces and teamwork
  • Setting up Spaces for topics, projects or client requests
  • Uploading your own documents and combining them with web sources
  • Storing instructions for consistent output formats
  • Sharing research and turning it into pages or reports
Module 4 Deep Research and structured reports
  • Tackling broad questions with Deep Research
  • Producing market overviews, competitor comparisons and topic briefs
  • Turning results into tables, side-by-side comparisons and executive summaries
  • Recognising the limits of in-depth research and refining results
Module 5 Fact-checking, data protection and workflow
  • Systematic fact-checking: counter-research and spotting contradictions
  • Typical failure patterns: outdated, misattributed or fabricated details
  • Data protection, enterprise licences and handling confidential queries
  • A reusable research process and source documentation
Module 6 Practice lab: Finance & Treasury
  • Cash flow commentary
  • Financing agreement analysis
  • Scenarios and sensitivities
  • Management and board reporting
  • Automated remittance matching
  • Policy and process documentation
Module 7 Data protection, quality & transfer to daily work
  • Financial and banking data is confidential and may constitute inside information under MAR; only use approved AI environments with a data processing agreement and GDPR-compliant data handling. Payment approvals must remain under the four-eyes principle and must never be triggered autonomously by AI agents.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Cash flow commentary

An AI assistant analyses variances between forecast and actual cash positions and drafts commentary on the main drivers, such as customer receipts, supplier payments or tax dates, which the team then reviews and refines.

Financing agreement analysis

Loan agreements, promissory notes or lease contracts are summarised: maturities, interest mechanics, covenants, termination rights and reporting obligations are extracted into a structured overview for treasury and controlling.

Scenarios and sensitivities

AI helps draft scenario narratives and spreadsheet formulas for interest rate, currency and commodity sensitivities, for example showing how rising reference rates would affect financing costs and covenant ratios.

Management and board reporting

From KPIs, tables and bullet points, an AI assistant produces concise executive summaries, slide drafts and explanatory notes for management, the board or lenders, tailored to audience and level of detail.

Automated remittance matching

An automation workflow reads remittance advices from emails and PDFs, matches them to open items and flags discrepancies for manual review before the data is posted to the ERP or cash management system.

Policy and process documentation

Treasury policies, payment approval processes and control descriptions are structured and harmonised with AI support from notes, existing documents and interviews, making them clear and audit-ready.

Prerequisites

Experience with online research in a professional context; no prior AI knowledge is required. You have hands-on experience in financial planning, treasury or reporting and are confident using spreadsheets.

Technical requirements

A laptop with an up-to-date browser and a Perplexity account; we recommend Pro or Enterprise access to use Spaces with files and Deep Research in full.

Data protection & compliance

Financial and banking data is confidential and may constitute inside information under MAR; only use approved AI environments with a data processing agreement and GDPR-compliant data handling. Payment approvals must remain under the four-eyes principle and must never be triggered autonomously by AI agents.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 Online Request
  • Thu, 3 December 2026 Kaiserslautern Request
  • Tue, 8 December 2026 Online Request
  • Mon, 14 December 2026 Bremen Request
  • Mon, 11 January 2027 Online Request
  • Tue, 26 January 2027 Salzburg Request

Frequently asked questions

How does Perplexity differ from ChatGPT or a search engine?

A search engine returns a list of links, and a general chat assistant often answers from its training data. Perplexity searches the web at the moment you ask, summarises what it finds and backs each statement with citations. That makes it particularly well suited to current, verifiable research – although you still need to assess the sources yourself.

Can I rely on Perplexity's citations?

Citations make verification much easier, but they do not replace it. Occasionally a statement is attributed to a source that does not say quite that, or an outdated page is used. In the course you learn a lean fact-checking routine that lets you quickly verify critical statements against the original source.

Is Perplexity suitable for confidential research within a company?

Enterprise plans offer additional commitments on data use and administration. Even so, you should not put trade secrets or personal data into queries unless your internal policy explicitly allows it. We discuss how to phrase queries so that sensitive details stay protected while the research remains useful.

Can an AI assistant approve payments on its own?

No. AI can prepare payment proposals, match remittances and flag anomalies, but payment approval stays within your authorisation and four-eyes framework. In the course we show how to design automations so that people remain at the critical control points and every action is logged and traceable for auditors.

How should we handle confidential financial data in AI tools?

Start with an approved enterprise environment that has a contractual data processing agreement and does not use your inputs for model training. Add rules for pseudonymisation, such as masking account numbers or counterparties. We discuss practical guidelines that you can agree directly with IT, data protection and compliance.

How much does “Perplexity for Finance Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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